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Book
Dynamics under Uncertainty: Modeling Simulation and Complexity
Authors: --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc.

Keywords

Research & information: general --- Mathematics & science --- Fuzzy MARCOS --- Fuzzy PIPRECIA --- traffic risk --- TFN --- MCDM --- dual-rotor --- multi-frequency excitation --- non-intrusive calculation --- metamodel --- NDSL model --- AHP --- criteria weights --- pairwise comparisons --- AES --- PC --- MIMO discrete-time system --- state feedback and output feedback --- parameter dependence --- D numbers --- fuzzy sets --- DEMATEL --- multi-criteria decision-making --- multi-criteria optimization --- RAFSI method --- performance comparison --- rank reversal --- Magnetic Resonance Imaging (MRI) --- wavelet transform --- GARCH --- LLA --- LDA --- KNN --- BWM --- BWM-I --- multi-criteria --- renewable energy --- the CCSD method --- the ITARA method --- the MARCOS method --- stackers --- logistics --- ensemble techniques --- data mining --- classification and discrimination --- linear regression --- applied mathematics general --- prediction theory --- theory of mathematical modeling --- medical applications --- empathic building --- fuzzy grey cognitive maps --- Thayer's emotion model --- artificial emotions --- affective computing --- Fuzzy MARCOS --- Fuzzy PIPRECIA --- traffic risk --- TFN --- MCDM --- dual-rotor --- multi-frequency excitation --- non-intrusive calculation --- metamodel --- NDSL model --- AHP --- criteria weights --- pairwise comparisons --- AES --- PC --- MIMO discrete-time system --- state feedback and output feedback --- parameter dependence --- D numbers --- fuzzy sets --- DEMATEL --- multi-criteria decision-making --- multi-criteria optimization --- RAFSI method --- performance comparison --- rank reversal --- Magnetic Resonance Imaging (MRI) --- wavelet transform --- GARCH --- LLA --- LDA --- KNN --- BWM --- BWM-I --- multi-criteria --- renewable energy --- the CCSD method --- the ITARA method --- the MARCOS method --- stackers --- logistics --- ensemble techniques --- data mining --- classification and discrimination --- linear regression --- applied mathematics general --- prediction theory --- theory of mathematical modeling --- medical applications --- empathic building --- fuzzy grey cognitive maps --- Thayer's emotion model --- artificial emotions --- affective computing


Book
Assessment of Energy–Environment–Economy Interrelations
Author:
ISBN: 3039288105 3039288091 Year: 2020 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

Energy consumption and economic growth have been of great interest to researchers and policy-makers. Knowing the actual causal relationship between energy and the economy with respect to environmental degradation has important implications for modeling environmental and growth policies. The eleven chapters included herein aim to help researchers, academicians, and especially decision-makers to understand relevant issues and adopt appropriate methods to tackle and solve relevant environmental problems. Various methods from different disciplines are proposed and applied to various environmental and energy issues.


Book
Dynamics under Uncertainty: Modeling Simulation and Complexity
Authors: --- ---
Year: 2021 Publisher: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

Choose an application

Bookmark

Abstract

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc.

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